13 Big Data Vendors To Watch In 2013

From Amazon to Splunk, here's a look at the big data innovators that are now pushing Hadoop, NoSQL and big data analytics to the next level.

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Pioneers Push Big Data EnvelopeThere are leaders and there are followers in the big data movement. This collection comprises a baker's dozen leaders. Some, like Amazon, Cloudera and 10Gen, were there at the dawn of the Hadoop and NoSQL movements. Others, like Hortonworks and Platfora, are newcomers, but draw on deep experience.

The three big themes you'll find in this collection are Hadoop maturation, NoSQL innovation and analytic discovery. The Hadoop crowd includes Cloudera, HortonWorks and MapR, each of which is focused entirely on bringing this big data platform to a broader base of users by improving reliability, manageability and performance. Cloudera and Hortonworks are improving access to data with their Impala and HCatalog initiatives, respectively, while MapR's latest push is improving HBase performance.

The NoSQL set is led by 10Gen, Amazon, CouchBase, DataStax and Neo Technologies. These are the developers and support providers behind MongoDB, DynamoDB, CouchBase, Cassandra and Neo4j, respectively, which are the leading document, cloud, key value, column and graph databases.

Big data analytic discovery is still in the process of being invented, and the leaders here include Datameer, Hadapt, Karmasphere, Platfora and Splunk. The first four have competing visions of how we'll analyze data in Hadoop, while the last specializes in machine-data analysis.

What you won't find here are old-guard vendors from the relational database world. Sure, some of those big-name companies have been fast followers. Some even have software distributions and have added important capabilities. But are their hearts really in it? In some cases, you get the sense that their efforts are window dressing. There are vested interests -- namely license revenue -- in sticking with the status quo, so you just don't see them out there aggressively selling something that just might displace their cash cows. In other cases, their ubiquitous connectors to Hadoop seem like desperate ploys for some big data cachet.

For many users, the key issues include flexibility, speed and ease of use. And it isn't clear that any single product or service can offer all of those capabilities at the moment.

We're still in the very early days of the big data movement, and as the saying goes, the pioneers might get the arrows while the settlers get the land. In our eyes, first movers like Amazon and Cloudera already look like settlers, and more than a few others on this list seem to have solid foundations in place. As we've seen before, acquisitions could change the big data landscape very quickly. But as of now, these are 13 big data pioneers that we're keeping our eyes on in 2013.

Good Article Douge, although it would be helpful to lay the information in one page as a summary, as I got confused scanning through the 13 pages, to get the differences, the following blog, tries to do so : http://mostafaelganainy.wordpress.com/2014/03/20/bigdata-evolution-from-mapreduce-to-infinity/

But the information isn't rich as in your article, so, I'll write another one, to get better understanding of the differences between the 13 tools

Does it seem much of this is expanding beyod the large scale enterprise? There are so many that claim to play in the Big Data space, My inevitable concern surrounds M&A activity in the space as well as being able to dumb the products down enough to sell into the mid market and smaller space.

Let's be sure to consider vendors not on the list who have been building and deploying complex big data systems around the world for the intel community before big data was even called big data! For example, Leidos (formerly SAIC) coupled more than 10 years of intel community big data experience with the latest in cloud and big data expertise with the release of DigitalEdge.

DigitalEdge is focused on the ingest of very large disparate data sets - streaming or batch - and preparing the data for analysis with whatever back-end analytics or visualization tools currently exist within your infrastructure. Bad data in means bad data being analyzed. DigitalEdge focuses on ensuring that the data that is ingested is joined (at ingest, not at query time), enriched and correlated to assert full context and completeness of the data prior to analysis.

With all the attention placed on analytics and visualization of big data, let's be sure to focus attention on the complexities of ingest, data preparation and management. Afterall, your analysis is only as good as the data going in!

The problem is that manual analysis can't keep up with Big Data. See this recent GigaOm article on this: http://gigaom.com/data/a-start.... In traditional Business Intelligence / Analytics, manual exploration is the weak link that slows speed-to-insight and risks overlooking key insights. In a traditional analysis process, even with the latest solutions, a human has to be involved in teasing out and testing relevant insights. As long as the analyst is the driver of the analysis, even when the query is instantaneous, the speed of getting to insights is limited by human speed while the quality of insights is limited by human bias and error. In an hour, a human can only explore just dozens of patterns out of millions of possibilities. In minutes, Lucid explores all of those millions of possibilities and presents the most important insights, without the risk of human bias or error. The human can then look through the short list of insights presented and further refine the most relevant insights.

A good list 13 Big Data pioneers, innovators and implementers of 2012 and surely they will bring much more in 2013.

Also, there is no doubt that in 2013 there is much more to see about Big data Analytics. Compared to 2011, in 2012 we saw some giants investing in Big Data research and development and I believe 2013 will bring more and more number of small-mid-larger organizations implementing Big Data for better results.

There are some companies who many not be in the list but are actively working on Big Data implementations for leaders in industry. Lets wait for 2013 to see what happens next....

The discussion around Big Data has certainly been a hot one this year and a lot of the focus has been on the data infrastructure. So much, that many CIOs think that Big Data is synonymous with Hadoop - which is not true.

The next chapter in Big Data is about "what you can do with Big Data" - it's about the Analytics. In my humble opinion, Analytics is the "killer app" for Big Data and I'd love to see more about the revolution happening in that sphere.

Big Data is indeed hot...and it's time for the industry to switch to the conversation about 'what to do with all this Data". In my humble opinion, the "killer app" for Big Data is Analytics!

SiSense is a top tier company in this space and hundreds of companies have chosen our platform - including international leaders like Target to innovative startups like Wix. You can find out more about us @ www.SiSense.com

Excellent point about why the old-guard vendors from the relational database world were excluded from your Top 13 Big Data Vendors to watch in the coming year. The new capabilities some RDBMSs have added falls short in comparison to how NoSQL vendors handle the unstructured aspect of Big Data. Missing from the list is MarkLogic, which for more than a decade has delivered a powerful and trusted enterprise-grade NoSQL database that enables organizations to turn all data into valuable and actionable information. After all, Big Data is only valuable if you can find the information that is critical to your organizationG«÷s success.

Most IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.

Why should big data be more difficult to secure? In a word, variety. But the business wonít wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.